Description Usage Arguments Details Value References See Also

Samples approximate second-order multivariate Gaussian knockoff variables for the original variables.

1 2 | ```
MFKnockoffs.create.approximate_gaussian(X, method = c("asdp", "equi", "sdp"),
shrink = F)
``` |

`X` |
normalized n-by-p realization of the design matrix |

`method` |
either 'equi', 'sdp' or 'asdp' (default:'asdp') This will be computed according to 'method', if not supplied |

`shrink` |
whether to shrink the estimated covariance matrix (default: FALSE) |

If the argument `shrink`

is set to TRUE, a James-Stein-type shrinkage estimator for
the covariance matrix is used instead of the traditional maximum-likelihood estimate. This option
requires the package `corpcor`

. Type `?corpcor::cov.shrink`

for more details.

Even if the argument `shrink`

is set to FALSE, in the case that the estimated covariance
matrix is not positive-definite, this function will apply some shrinkage.

To use SDP knockoffs, you must have a Python installation with
CVXPY. For more information, see the vignette on SDP knockoffs:
`vignette('sdp', package='MFKnockoffs')`

n-by-p matrix of knockoff variables

Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html

Other methods for creating knockoffs: `MFKnockoffs.create.fixed`

,
`MFKnockoffs.create.gaussian`

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